With the advent in technological advancements over the past few decades, there has been an exponential rise in the number of large facilities. These facilities are big residential complexes, commercial offices, shopping centers and the like. The energy consumption of these facilities is usually very high. These large facilities include many electrical and mechanical devices which require huge amount of electrical power. A slight increase or decrease increase in the energy consumption of these facilities makes a huge impact commercially.
In general, a consistent check on the energy consumption pattern or energy audit in these large facilities is maintained. The owner/administrator of the large facility usually hires a consultancy firm/a third party service provider/an in-house team to assess the energy consumption, future energy consumption, probable saving in the future energy consumption, profit and expenses that would be incurred to save the future probable energy consumption, and so on. Presently, the energy auditing techniques employed by these third party service providers provide a set of periodic recommendations for improving the energy consumption. The third party service providers perform audit by collecting, analyzing, recognizing and recommending a number of changes in the energy consumption which can bring down the overall energy consumption rate.
These recommendations are highly inaccurate and do not bring about a substantial effect to the overall energy consumption. The existing systems do not take into account an occupancy behavior and energy consumption pattern for users inside the facilities. Further, these existing systems are unable to capture energy consumption data associated with each and every energy consuming device present inside the facilities. The current data collections methods are archaic and there is a consistent need to develop better data collection methods which can collect data from each and every device in real time.
In addition, a proper communication and feedback control system is either limited or absent in these systems. This leads to cost escalation and frequent manual maintenance of electrical and mechanical systems, thus employing bulk of users. Moreover, the existing systems do not take into account present weather conditions and seasonal energy consumption while recommending one or more changes in energy consumption inside the large facilities. The administrators have no idea about which energy consuming devices consume most of the energy which does not provision the users to effectively reduce the energy costs. Further, the administrators have no idea how to maintain a balance between the energy consumption of various devices in different seasons.
In light of the above stated discussion, there is a need for a method and system that overcomes the above stated disadvantages.